BEA API
Arvind Sharma
2024-08-03
https://github.com/us-bea/bea.R
1 SetUp
# Clear the workspace
rm(list = ls()) # Clear environment -remove all files from your workspace
gc() # Clear unused memory## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 551262 29.5 1224912 65.5 NA 700242 37.4
## Vcells 1021331 7.8 8388608 64.0 32768 1963400 15.0 cat("\f") # Clear the console# graphics.off() # Clear all graphs2 Library
library("bea.R") # for getting data ## Loading required package: data.table## Note: As of February 2018, beaGet() requires 'TableName' for NIPA and NIUnderlyingDetail data instead of 'TableID.' See https://github.us-bea/bea.R for details.library("tidyverse") # data manipulation/wrangling## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::between() masks data.table::between()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::first() masks data.table::first()
## ✖ lubridate::hour() masks data.table::hour()
## ✖ lubridate::isoweek() masks data.table::isoweek()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::last() masks data.table::last()
## ✖ lubridate::mday() masks data.table::mday()
## ✖ lubridate::minute() masks data.table::minute()
## ✖ lubridate::month() masks data.table::month()
## ✖ lubridate::quarter() masks data.table::quarter()
## ✖ lubridate::second() masks data.table::second()
## ✖ purrr::transpose() masks data.table::transpose()
## ✖ lubridate::wday() masks data.table::wday()
## ✖ lubridate::week() masks data.table::week()
## ✖ lubridate::yday() masks data.table::yday()
## ✖ lubridate::year() masks data.table::year()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors3 My Unique BEA API key
Replace with your own unique key that you got on signup.
my_beaKey <- '293963AB-94AA-4234-AF30-43EB482D85CB'4 Search for the data tables
?beaSearch
beaSearch(searchTerm = 'GDP',
beaKey = my_beaKey,
asHtml = TRUE
)## Creating first-time local copy of metadata for all datasets - only done once.## Datasets will be updated only if timestamps indicate metadata obsolete in future searches,## and only obsolete metadata sets will be updated (it's faster this way).## ## No encoding supplied: defaulting to UTF-8.## Warning in beaSearch(searchTerm = "GDP", beaKey = my_beaKey, asHtml = TRUE):
## Regional metadata is missing from
## /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library/beaR/data
## and may be locked for updating on the BEA API; searching national metadata
## only.5 Load the data tables
# beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10705', ...))
?beaGet
my_beaSpecs <- list(
'UserID' = my_beaKey ,
'Method' = 'GetData',
'datasetname' = 'NIPA',
'TableName' = 'T10705',
'Frequency' = 'Q',
'Year' = 'X',
'ResultFormat' = 'json'
)
beaPayload <- beaGet(beaSpec = my_beaSpecs)## No encoding supplied: defaulting to UTF-8.6 Transform the wide data into long form
beaLong <- beaGet(beaSpec = my_beaSpecs,
asWide = FALSE
)## No encoding supplied: defaulting to UTF-8.table(beaLong$LineDescription)##
## Average of GDP and GDI
## 309
## Business current transfer payments (net)
## 310
## Capital consumption allowances
## 310
## Contributions for government social insurance, domestic
## 310
## Corporate profits with inventory valuation and capital consumption adjustments
## 309
## Current surplus of government enterprises
## 262
## Domestic business
## 310
## Equals: Gross national product
## 309
## Equals: National income
## 309
## Equals: Net national product
## 309
## Equals: Personal income
## 310
## General government
## 310
## Government
## 310
## Government enterprises
## 310
## Gross domestic income (GDI)
## 309
## Gross domestic product (GDP)
## 310
## Gross national factor income
## 309
## Gross national income
## 309
## Households and institutions
## 310
## Less: Capital consumption adjustment
## 310
## Less: Consumption of fixed capital
## 310
## Less: Income payments to the rest of the world
## 309
## Less: Statistical discrepancy
## 309
## Net domestic income
## 309
## Net domestic product
## 310
## Net domestic purchases
## 310
## Net interest and miscellaneous payments on assets
## 310
## Net national factor income
## 309
## Plus: Income receipts from the rest of the world
## 309
## Plus: Personal current transfer receipts
## 310
## Plus: Personal income receipts on assets
## 310
## Private
## 310
## Statistical discrepancy as a percentage of GDP
## 309
## Taxes on production and imports less subsidies
## 3107 Graph
df <- beaLong |> filter(LineDescription =="Gross domestic product (GDP)")
df$year <- substring(text = df$TimePeriod,
first = 1,
last = 4
)
ggplot(data = df,
mapping = aes(x = year,
y = DataValue/1000000)
) + geom_line() + labs(title = "US GDP over time",
x = "Year",
y = "GDP (in trillions)"
) +
theme(axis.text.x = element_text(angle=90,
vjust=.5)
)
7.1 Transform the data
?aggregate
# Aggregate the data by year and calculate the mean of DataValue
df_annual <- aggregate(DataValue ~ year,
data = df,
FUN = mean)
ggplot(data = df_annual,
mapping = aes(x = as.numeric(year),
y = DataValue/1000000)
) + geom_line() + labs(title = "US GDP over time",
x = "Year",
y = "GDP (in trillions)"
) +
theme(axis.text.x = element_text(angle=90,
vjust=.5)
)